GPU Memory Hierarchy and Optimization

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Data-Parallel Programming: Vector & SIMD Processors

Explores data-parallel programming with vector processors and SIMD, and introduces MapReduce, Pregel, and TensorFlow.

Memory Consistency Models: Performance Impact and Parallel Computing

Covers memory consistency models, parallel computing, atomic subroutines, GPU architecture, and multithreading.

Virtual Memory

Covers virtual memory, program relocation, memory protection, TLB, and memory hierarchy interactions.

Memory Cache Principles

Explores memory cache principles, emphasizing spatial locality, latency impact, and cache efficiency strategies.

Advanced Multiprocessor Architecture

Covers Advanced Multiprocessor Architecture, discussing course logistics, components, grading, and trends in modern computing systems.

VLIW Architectures: Compilers and Instruction Level Parallelism

Covers VLIW architectures and compilers, focusing on instruction level parallelism and optimization techniques.

Challenges in High-Level Synthesis: Optimizing Loop Constructs

Addresses the challenges of high-level synthesis and optimizing loop constructs using the polyhedral model for improved performance and scheduling.

Cache Coherence

Covers cache coherence in multiprocessor systems and the challenges of maintaining coherence and consistency in modern processors.

GPU Introduction: Performance and Programming

Explores the motivation and advantages of using GPUs for computation, focusing on their performance and programming through CUDA.

Multithreading: Pipeline Utilization and Performance Impact

Explores multithreading in computer architecture, focusing on pipeline utilization and performance impact in various techniques, including blocked and fine-grained multithreading.